02 - Alpha Diversity Analysis

Author

Francesc Català-Moll

Published

April 1, 2025

Modified

April 1, 2025

1 Define the input and output paths

# input
tse_file <- here::here("data", "processed", "tse_cleaned.rds")
igc_file <- here::here("data", "raw", "dataTable.rds")

# output
out_dir <- here::here("data", "02_alpha_diversity")
dir.create(out_dir, showWarnings = FALSE, recursive = TRUE)

2 Set-up

# load libraries
library(magrittr)
library(patchwork)
suppressPackageStartupMessages(library(tidySingleCellExperiment))

3 Computate Alpha Diversity Metrics

3.1 Load TreeSE

# load
tse <- readr::read_rds(tse_file)

3.2 Add Gene Richness

# load
igc_df <- readr::read_rds(igc_file) %>% tibble::as_tibble()

# threshold
threshold <- 
  igc_df %>%
  dplyr::group_by(SampleID) %>% 
  dplyr::summarise(NumberMappedReads = max(NumberMappedReads)) %>% 
  dplyr::summarise(q = quantile(NumberMappedReads, 0.02)) %>%
  dplyr::pull()
  
# extract
igc_df <- 
  igc_df %>% 
  dplyr::filter(ReadCountReal >= threshold) %>% 
  dplyr::group_by(SampleID) %>%
  dplyr::summarise(gene_richness = min(GeneNumber))

# merge
tse <- tse %>% dplyr::left_join(igc_df, by = "SampleID")
Loading required namespace: TreeSummarizedExperiment

3.3 Compute Alpha Diversity Metrics

# select metrics
metrics <- c(
  "shannon_diversity", "gini_dominance","observed_richness", "pielou_evenness"
)

# compute
tse <-
  tse %>%
  mia::addAlpha(
    index = metrics,
    sample = quantile(colSums(assay(tse, "counts")), 0.02),
    niter = 10,
    BPPARAM = BiocParallel::MulticoreParam()
  )

4 Plot Alpha Diversity Metrics

plt_list <- 
  c(metrics, "gene_richness") %>% 
  purrr::set_names() %>% 
  purrr::map( ~ {
    # prepare data
    plt_df <- colData(tse) %>% tibble::as_tibble() 
     
    # calculate stats
    stats_2 <-
      plt_df %>%
      rstatix::group_by(treatment) %>%
      rstatix::wilcox_test(
        formula = formula(glue::glue("{.x} ~ time_point")),
        p.adjust.method = "fdr"
      ) %>%
      rstatix::add_significance() %>%
      rstatix::add_xy_position(x = "time_point", group = "treatment") %>% 
      dplyr::mutate(y.position = y.position)
      
    # plot
    plt <- 
      plt_df %>% 
      tidyr::drop_na(!!dplyr::sym(.x)) %>% 
      tidyplots::tidyplot(x = time_point, y = !!dplyr::sym(.x), colour = treatment) %>% 
      tidyplots::add_boxplot(show_outliers = FALSE) %>%
      tidyplots::add_data_points_jitter(alpha = 0.4) %>%
      tidyplots::add_test_asterisks(
        method = "wilcox_test", hide_info = TRUE, bracket.nudge.y = 0.05, tip.length = 0.01
      ) %>%
      tidyplots::add(ggpubr::stat_pvalue_manual(
        stats_2, label = "p.adj.signif", hide.ns = TRUE, tip.length = 0.01,
      )) %>% 
      tidyplots::adjust_x_axis_title("Time Point (weeks)") %>%
      tidyplots::adjust_legend_title("Treatment") %>%
      tidyplots::adjust_y_axis_title(
        .x %>% stringr::str_replace_all("_", " ") %>% stringr::str_to_title()
      ) %>% 
      tidyplots::adjust_colors(tidyplots::colors_discrete_friendly) 
      
    if (.x == "gene_richness") {
      plt <- plt %>% tidyplots::adjust_y_axis(labels = scales::scientific)
    }
    
    # save
    plt %>%
      tidyplots::adjust_size(width = 40, height = 40, unit = "mm") %>%
      tidyplots::save_plot(
        here::here(out_dir, glue::glue("alpha_{.x}.pdf")),
        view_plot = FALSE
      )
    
    plt
  })

5 Compute Correlations

5.1 Define Cytokines and Markers

populations <- c("CD4", "CD8", "CD4_nadir", "CD4_CD38_DR", "CD8_CD38_DR")
cytokines <- c("CRP", "IL6", "TNFa", "sCD14")
others <- c("HIV_VL", "BMI")

all_markers <- c(populations, cytokines, others)

5.2 Cytokines Log2 Transformation

tse2 <- tse %>% dplyr::mutate(dplyr::across(dplyr::all_of(cytokines), ~ log2(.x + 1)))

5.3 Compute Correlations

corr_df <-
  mia::getCrossAssociation(
    tse2,
    tse2,
    col.var1 = c(metrics, "gene_richness"),
    col.var2 = all_markers,
    method = "spearman",
    p_adj_method = "fdr",
    test.signif = TRUE,
    verbose = FALSE,
    show_warnings = FALSE
  )

5.4 Plot Global Correlations

# prepare data
plt_df <- corr_df %>% dplyr::mutate(sign = p_adj < 0.05 & abs(cor) >= 0.2)

# plot
lim <- abs(plt_df$cor) %>% max() %>% round(digits = 1) * 1.05
plt <-
  plt_df %>%
  dplyr::mutate(lab = stringr::str_replace(Var1, "_", " ") %>% stringr::str_to_sentence()) %>% 
  ggplot(aes(x = Var2, y = lab, fill = cor, colour = sign)) +
  geom_point(aes(size = abs(cor), alpha = sign), shape = 22, stroke = 1) +
  scale_fill_gradientn(
    colours = rev(RColorBrewer::brewer.pal(11, "PiYG")),
    limits = c(-lim, lim)
  ) +
  scale_colour_manual(values = c("TRUE" = "black", "FALSE" = "white")) +
  scale_alpha_manual(values = c("TRUE" = 0.9, "FALSE" = 0.6), guide = "none") +
  scale_size_continuous(range = c(2, 8), guide = "none") +
  theme_minimal() +
  labs(
    x = "", y = "", fill = "Spearman Correlation", colour = "q < 0.05"
  ) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "top",
    legend.title.position = "top",
    legend.title = element_text(hjust = 0.5),
    legend.key.height = unit(0.5, "cm"),
  )

# Resize and save
r_plt <-
  plt + plot_spacer() +
  plot_layout(
    widths = ggplot2::unit(c(75, 1), "mm"),
    heights = ggplot2::unit(40, "mm")
  )

tidyplots::save_plot(
  r_plt,
  here::here(out_dir, glue::glue("all_correlation.pdf")),
  view_plot = FALSE
)

r_plt

6 Compute Correlations by Treatment Group

6.1 Compute Correlations

corr_df <-
  mia::splitOn(tse2, "treatment") %>%
  purrr::map_dfr(~ {
    mia::getCrossAssociation(
      .x,
      .x,
      col.var1 = c(metrics, "gene_richness"),
      col.var2 = all_markers,
      method = "spearman",
      p_adj_method = "fdr",
      test.signif = TRUE, 
      verbose = FALSE, 
      show_warnings = FALSE
    ) %>%
      dplyr::mutate(treatment = unique(.x$treatment))
  })

6.2 Plot Global Correlations

plt_list <- 
  c(metrics, "gene_richness") %>% 
  purrr::set_names() %>% 
  purrr::map(~ {
    # prepare data
    plt_df <-
      corr_df %>% 
      dplyr::filter(Var1 == .x) %>% 
      dplyr::mutate(sign = p_adj < 0.05 & abs(cor) >= 0.2) 
      
    # plot
    .name <- stringr::str_replace(.x, "_", " ") %>% stringr::str_to_title()
    lim <- abs(plt_df$cor) %>% max() %>% round(digits = 1)
    plt <- 
      plt_df %>% 
      ggplot(aes(x = Var2, y = treatment, fill = cor, colour = sign)) +
      geom_point(aes(size = abs(cor), alpha = sign), shape = 22, stroke = 1) +
      scale_fill_gradientn(
        colours = rev(RColorBrewer::brewer.pal(11, "PiYG")),
        limits = c(-lim, lim)
      ) +
      scale_colour_manual(values = c("TRUE" = "black", "FALSE" = "white")) +
      scale_alpha_manual(values = c("TRUE" = 0.9, "FALSE" = 0.6), guide = "none") +
      scale_size_continuous(range = c(2, 8), guide = "none") +
      theme_minimal() +
      labs(
        x = "",
        y = "Treatment", 
        fill = glue::glue("Spearman Correlation\n(Mark. vs. {.name})"), 
        colour = "q < 0.05"
      ) +
      theme(
        axis.text.x = element_text(angle = 45, hjust = 1), 
        legend.position = "top", 
        legend.title.position = "top", 
        legend.title = element_text(hjust = 0.5), 
        legend.key.height = unit(0.5, "cm"),
      ) 
    
    # Resize and save
    plt <- 
      plt + plot_spacer() +
      plot_layout(
        widths = ggplot2::unit(c(70, 1), "mm"), 
        heights = ggplot2::unit(30, "mm")
      ) 
    
    tidyplots::save_plot(
      plt, 
      here::here(out_dir, glue::glue("correlation_{.x}.pdf")), 
      view_plot = FALSE
    )
    
    plt
  })

6.3 Plot Scatter Correlations

plt_df <- colData(tse2) %>% tibble::as_tibble()
plt_list <- 
  c(metrics, "gene_richness") %>% 
  purrr::set_names() %>% 
  purrr::map(function(.metric) {
   all_markers %>% 
      purrr::set_names() %>% 
      purrr::map(function(.marker) {
        # prepare data
        marker_values <- plt_df[[.marker]] %>% .[is.finite(.)]
        lab_min <- min(marker_values, na.rm = TRUE)
        lab_max <- max(marker_values, na.rm = TRUE)
        lab_range <- lab_max - lab_min
        
        .name <- stringr::str_replace(.metric, "_", " ") %>% stringr::str_to_title()
        .y_lab <- dplyr::if_else(
          .marker %in% cytokines, glue::glue("Log2  {.marker}"), .marker
        )
        
        plt <- 
          plt_df %>%
          tidyr::drop_na(!!.metric, !!.marker) %>%
          tidyplots::tidyplot(
            x = !!dplyr::sym(.metric),
            y = !!dplyr::sym(.marker),
            colour = treatment
          ) %>%
          tidyplots::add_data_points(alpha = 0.5) %>%
          tidyplots::add(geom_smooth(method = "lm", alpha = 0.1, formula = 'y ~ x')) %>%
          tidyplots::add(ggpubr::stat_cor(
            method = "spearman",
            cor.coef.name = "rho",
            label.y.npc = "bottom",
            p.digits = 1,
            label.y = c(lab_min - 0.05 * lab_range, lab_min - 0.15 * lab_range), 
            size = 3
          )) %>% 
          tidyplots::adjust_legend_title("Treatment") %>%
          tidyplots::adjust_y_axis_title(.y_lab) %>% 
          tidyplots::adjust_x_axis_title(.name) %>% 
          tidyplots::adjust_x_axis(rotate_labels = 30) %>% 
          tidyplots::adjust_colors(tidyplots::colors_discrete_friendly) 
          
          if (.metric == "gene_richness") {
            plt <- plt %>% tidyplots::adjust_x_axis(labels = scales::scientific)
          }
      
          # save
          plt %>%
            tidyplots::adjust_size(width = 50, height = 40, unit = "mm") %>%
            tidyplots::save_plot(
              here::here(out_dir, glue::glue("corr_{.metric}_{.marker}.pdf")),
              view_plot = FALSE
            )
          
          plt
      })
  })

7 Export TSE with Alpha Metrics

tse %>% readr::write_rds(here::here("data", "processed", "tse_alpha.rds"))

8 Appendix

8.1 Session Info

devtools::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.0 alpha (2025-03-25 r88054)
 os       Ubuntu 22.04.5 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Europe/Madrid
 date     2025-04-01
 pandoc   3.2 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/x86_64/ (via rmarkdown)
 quarto   1.5.57 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
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 TreeSummarizedExperiment   2.15.0   2024-10-29 [1] Bioconduc~
 ttservice                * 0.4.1    2024-06-07 [1] RSPM (R 4.5.0)
 tzdb                       0.5.0    2025-03-15 [1] RSPM (R 4.5.0)
 UCSC.utils                 1.3.1    2025-01-15 [1] Bioconduc~
 urlchecker                 1.0.1    2021-11-30 [1] RSPM
 usethis                    3.1.0    2024-11-26 [1] RSPM
 vctrs                      0.6.5    2023-12-01 [1] RSPM (R 4.5.0)
 vegan                      2.6-10   2025-01-29 [1] RSPM (R 4.5.0)
 vipor                      0.4.7    2023-12-18 [1] RSPM
 viridis                    0.6.5    2024-01-29 [1] RSPM
 viridisLite                0.4.2    2023-05-02 [1] RSPM (R 4.5.0)
 withr                      3.0.2    2024-10-28 [1] RSPM (R 4.5.0)
 xfun                       0.51     2025-02-19 [1] RSPM
 xml2                       1.3.8    2025-03-14 [1] CRAN (R 4.5.0)
 xtable                     1.8-4    2019-04-21 [1] RSPM
 XVector                    0.47.2   2025-01-08 [1] Bioconduc~
 yaml                       2.3.10   2024-07-26 [1] RSPM
 yulab.utils                0.2.0    2025-01-29 [1] RSPM
 zoo                        1.8-13   2025-02-22 [1] RSPM

 [1] /home/fcatala/R/x86_64-pc-linux-gnu-library/4.5
 [2] /opt/R/next/lib/R/library
 * ── Packages attached to the search path.

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8.2 Contact

  • Analysis Lead: Francesc Català-Moll
  • Email: fcatala@irsicaixa.es
  • Institution: GEM - IrsiCaixa